用VR监测学生表情与学习数据，实现自适应教学。
SMARTe-VR: Student Monitoring and Adaptive Response Technology for e-Learning in Virtual Reality
- 通过面部生物特征和学习行为数据捕捉学生状态。
- 收集超25小时数据，包含450次答题与理解标签。
- 适合教育科技与虚拟现实研究者参考。
本文提出SMARTe-VR平台，用于沉浸式虚拟现实在线教育中的学生监测。该平台聚焦于面部生物特征与学习元数据的采集，支持教师创建定制化学习会话，包含视频讲座、自动问答系统（AutoQA）、互动工具（如文本高亮与讲座标记）及实时反馈。同时，我们发布了包含10名用户在基于VR的TOEIC课程中超过25小时的数据集，涵盖面部特征、学习元数据、450条问题回答、题目难度等级、概念标签与理解标签。针对理解检测，我们采用改进的项目反应理论模型，并探索两种架构：用于局部特征的时序卷积网络（TCN）与用于全局特征的多层感知机（MLP）。
原文摘要 · Abstract (English)
This work introduces SMARTe-VR, a platform for student monitoring in an immersive virtual reality environment designed for online education. SMARTe-VR aims to collect data for adaptive learning, focusing on facial biometrics and learning metadata. The platform allows instructors to create customized learning sessions with video lectures, featuring an interface with an AutoQA system to evaluate understanding, interaction tools (for example, textbook highlighting and lecture tagging), and real-time feedback. Furthermore, we released a dataset that contains 5 research challenges with data from 10 users in VR-based TOEIC sessions. This data set, which spans more than 25 hours, includes facial features, learning metadata, 450 responses, difficulty levels of the questions, concept tags, and understanding labels. Alongside the database, we present preliminary experiments using Item Response Theory models, adapted for understanding detection using facial features. Two architectures were explored: a Temporal Convolutional Network for local features and a Multilayer Perceptron for global features.
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